class_docstr: Bayesian ARD regression.
  
  Fit the weights of a regression model, using an ARD prior. The weights of
  the regression model are assumed to be in Gaussian distributions.
  Also estimate the parameters lambda (precisions of the distributions of the
  weights) and alpha (precision of the distribution of the noise).
  The estimation is done by an iterative procedures (Evidence Maximization)
  
  Read more in the :ref:`User Guide <bayesian_regression>`.
  
  Parameters
  ----------
  n_iter : int, optional
      Maximum number of iterations. Default is 300
  
  tol : float, optional
      Stop the algorithm if w has converged. Default is 1.e-3.
  
  alpha_1 : float, optional
      Hyper-parameter : shape parameter for the Gamma distribution prior
      over the alpha parameter. Default is 1.e-6.
  
  alpha_2 : float, optional
      Hyper-parameter : inverse scale parameter (rate parameter) for the
      Gamma distribution prior over the alpha parameter. Default is 1.e-6.
  
  lambda_1 : float, optional
      Hyper-parameter : shape parameter for the Gamma distribution prior
      over the lambda parameter. Default is 1.e-6.
  
  lambda_2 : float, optional
      Hyper-parameter : inverse scale parameter (rate parameter) for the
      Gamma distribution prior over the lambda parameter. Default is 1.e-6.
  
  compute_score : boolean, optional
      If True, compute the objective function at each step of the model.
      Default is False.
  
  threshold_lambda : float, optional
      threshold for removing (pruning) weights with high precision from
      the computation. Default is 1.e+4.
  
  fit_intercept : boolean, optional
      whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (e.g. data is expected to be already centered).
      Default is True.
  
  normalize : boolean, optional, default False
      If True, the regressors X will be normalized before regression.
      This parameter is ignored when `fit_intercept` is set to False.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      `preprocessing.StandardScaler` before calling `fit` on an estimator
      with `normalize=False`.
  
  copy_X : boolean, optional, default True.
      If True, X will be copied; else, it may be overwritten.
  
  verbose : boolean, optional, default False
      Verbose mode when fitting the model.
  
  Attributes
  ----------
  coef_ : array, shape = (n_features)
      Coefficients of the regression model (mean of distribution)
  
  alpha_ : float
     estimated precision of the noise.
  
  lambda_ : array, shape = (n_features)
     estimated precisions of the weights.
  
  sigma_ : array, shape = (n_features, n_features)
      estimated variance-covariance matrix of the weights
  
  scores_ : float
      if computed, value of the objective function (to be maximized)
  
  Examples
  --------
  >>> from sklearn import linear_model
  >>> clf = linear_model.ARDRegression()
  >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
  ... # doctest: +NORMALIZE_WHITESPACE
  ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
          copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
          n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001,
          verbose=False)
  >>> clf.predict([[1, 1]])
  array([ 1.])
  
  Notes
  --------
  See examples/linear_model/plot_ard.py for an example.
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code: def __init__(
      self,
      n_iter=300,
      tol=1.0e-3,
      alpha_1=1.0e-6,
      alpha_2=1.0e-6,
      lambda_1=1.0e-6,
      lambda_2=1.0e-6,
      compute_score=False,
      threshold_lambda=1.0e4,
      fit_intercept=True,
      normalize=False,
      copy_X=True,
      verbose=False,
  ):
      self.n_iter = n_iter
      self.tol = tol
      self.fit_intercept = fit_intercept
      self.normalize = normalize
      self.alpha_1 = alpha_1
      self.alpha_2 = alpha_2
      self.lambda_1 = lambda_1
      self.lambda_2 = lambda_2
      self.compute_score = compute_score
      self.threshold_lambda = threshold_lambda
      self.copy_X = copy_X
      self.verbose = verbose
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/linear_model/bayes.py
imports: from __future__ import print_function
  from math import log
  import numpy
  from scipy import linalg
  from .base import LinearModel
  from ..base import RegressorMixin
  from ..utils.extmath import fast_logdet, pinvh
  from ..utils import check_X_y
label_desc: Use image node_id 1 to create a new ARDRegression object from inherited base classes: LinearModel, RegressorMixin with example: obj = ARDRegression(n_iter, tol, alpha_1, alpha_2, lambda_1, lambda_2, compute_score, threshold_lambda, fit_intercept, normalize, copy_X, verbose)